Automated architectural space layout planning using a physics-inspired generative design framework
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Abstract (arXiv)
The determination of space layout is one of the primary activities in the schematic design stage of an architectural project. The initial layout planning defines the shape, dimension, and circulation pattern of internal spaces; which can also affect performance and cost of the construction. When carried out manually, space layout planning can be complicated, repetitive and time consuming. In this work, a generative design framework for the automatic generation of spatial architectural layout has been developed. The proposed approach integrates a novel physics-inspired parametric model for space layout planning and an evolutionary optimisation metaheuristic. Results revealed that such a generative design framework can generate a wide variety of design suggestions at the schematic design stage, applicable to complex design problems.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 64 references that are in the map. First posted on arXiv 21 Jun 2024.
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6 of 64 references.
References · 6
- 2017Generative Adversarial Networks: An Overviewcited by 588
- 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
- 2020House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generationcited by 86
- 2020TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domaincited by 65
- 2018CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networkscited by 31
- 2021ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Designcited by 12
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